Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early‐stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi‐square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K‐nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best‐fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time.

Cervical Cancer (CC) is the fourth major cancer, which is responsible for a large number of deaths among women. Early stage detection of the cancer is the most effective solutions for decreasing the mortality rate. The lack of awareness, and costly clinical diagnosis are the major barrier for women to participate in clinical screening test to detect CC at an early stage. To address the issue, the study aims to find a deep learning based intelligent system to detect CC in the early stage using real time images. To build the model, cervical cancer pap-smear test image datasets were gathered and these were labeled and preprocessed. Then the YOLOv5 model was employed on the labeled dataset to train the model. Three latest versions of YOLOv5 model were applied in this study to find the most efficient model for building the intelligent system to detect CC at an early stage. All of the applied models provided satisfactory performance. Among all the applied models, YOLOv5s outperformed with 0.8279 precision and 0.8265 recall value. The performance of the study indicates that the proposed model highly potential to diagnose CC using real time images in early stage. In the medical field, the proposed will be quite useful for clinicians, and medical professionals.

Autism Spectrum Disorder (ASD) is a set of neurological impairments which are incurable but can be improved with early treatment. We obtained slightly earlier detected ASD datasets pertaining to children and highly processed the dataset as needed. Various ML approaches were applied to the collected dataset and compared their performance based on accuracy, precision, recall, f-measure, log loss, kappa statistics, and MCC. We found that Random Forest and XGBoost provide the best performance with 97.70% accuracy. Model Interpretation methods were applied to interpret the models how they predict positive or negative class. In addition to that important feature and the impact of features on each class been found by shapely value. The impact of features on ASD is found to find the risk factors, which are mostly related or responsible for ASD. The study’s findings indicate that, when properly tuned, machine learning approaches can offer accurate forecasts of ASD status. According to the findings, the suggested model has the ability to diagnose ASD in its early phases.